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Yet Another Fog Simulator for Python.

Project description

YAFS (Yet Another Fog Simulator) is a simulator tool based on Python of architectures such as: Fog Computing ecosystems for several analysis regarding with the placement of resources, cost deployment, network design, … IoT environments are the most evident fact of this type of architecture.

The highlights points of YAFS are: * Dinamyc topology: entities and network links can be created or removed along the simulation. * Dinamyc creation of messages sources: sensors can generate messages from different point access along the simulation. * And for hence, the placement allocation algorithm and the orchestration algorithm, that are extended by the user, can run along the simulation. * The topology of the network is based on [Complex Network theory]( Thus, the algorithms can obtain more valuable indicators from topological features. * The results are stored in a raw format in a nosql database. The simpler the format, the easier it is to perform any type of statistics.

YAFS is released under the MIT License. However, we would like to know in which project or publication have you used or mentioned YAFS.

Getting started

YAFS tutorial ( is a good starting point for you. You can also try out some of the Examples ( shipped with YAFS but in any case you have to understand the main concepts of Cloud Computing and other related architectures to design and modelling your own model.

Documentation and Help

The documentation contains a [tutorial](, the architecture design ( explaining key concepts, a number of examples ( and the API reference (


Authors acknowledge financial support through grant project ORDCOT with number TIN2017-88547-P (AEI/FEDER, UE)

Changelog for YAFS

0.3 - 2018-12-03

  • One fix regarding the generation of random numbers.
  • Two new examples

0.2 - 2018-04-06

  • Several fixings in yafs structure
  • Some bugs fixed
  • 5 extensive examples

0.1 - 2017-11-21

  • Uploaded the first version (Isaac Lera and Carlos Guerrero)


YAFS was created by Isaac Lera and Carlos Guerrero along 2017-2018.

Project details

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